Suggesting Connections to a User Based on an Expected Value of the Suggestion to the Social Networking System

ABSTRACT

To suggest new connections to a user of a social networking system, the system generates a set of candidate users to whom the user has not already formed a connection. The system determines the likelihood that the user will connect to each candidate user if suggested to do so, and it also computes the value to the social networking system if the user does connect to the candidate user. Then, the system computes an expected value score for each candidate user based on the corresponding likelihood and the value. The candidate users are ranked and the suggestions are provided to the user based on the candidate users&#39; expected value scores. The social networking system can suggest other actions to a user in addition to forming a new connection with other users.

BACKGROUND

This invention relates generally to social networking systems, and moreparticularly to suggesting connections and/or interactions to users orobjects within a social networking system.

Users of social networking services may form connections, associations,or other relationships with other users based on real-life interactions,online interactions, or a mixture of both. For example, users may chooseto connect with others who may be from the same geographic location,travel in the same circle of friends, or may have attended the samecollege or university. Once connected within the social networkingsystem, users may preferentially interact with their connections bymessaging each other, posting content about each other, playinginteractive games with each other etc. Social networking systems valueuser connections because better connected users tend to use the socialnetworking system more, thus increasing user engagement and providing abetter user experience. Accordingly, it is generally desirable thatusers have many connections within the social networking system.

Although it is beneficial to have users with many connections within thesocial networking system, it may be difficult for users of a socialnetworking system to locate other users with whom they may wish to forma connection. Social networking systems generally provide a way forusers to find such connections. However, many of these methods are timeand labor intensive. For example, a user may be required to enter thename, email address, instant messaging ID, and other similar data tolocate another user within the social networking system. Since such amethod of locating connections is time and labor intensive, a user maychoose not to form certain connections within the social networkingsystem. Additionally, users may simply forget to locate some connectionswithin the social networking system.

Social networking systems do provide mechanisms for suggesting otherusers that a particular user may know. In such a system, one or moreother users may be suggested as possible connections to a particularuser if those other users are in the same social network or otherwiseappear likely to be known to the user. For example, if a user is part ofa university network, other users, also a part of the university networkbut not connected with the first user, may be suggested as potentialconnections for the user. Such a method of suggesting friends may reducethe time and labor associated with locating users within the socialnetworking system, but it may also provide irrelevant suggestions. Forexample, just because two users are part of a same university network,those users do not necessarily know each other in real life.

Another method of suggesting connections within a social networkingsystem is to determine the number of common connections between twousers. For example, if two users have a large number of commonconnections, the users may be suggested to each other as potentialconnections. However, such a system may not be any more precise than theother method of suggesting connections because it is not necessary thattwo users with a same mutual connection know or interact with each otheroutside the social networking system. Additionally, such a system isbiased against suggesting friends to a new user of a social networkingsystem or a user who many not have many connections within the socialnetworking system. For example, if a user only has two connectionswithin the social networking system, the user will at most have twoconnections in common with a user who has two hundred connections withinthe social networking system.

Although it is valuable to facilitate connections for users with fewconnections within the social networking system, a system that suggestsconnections based on the number of mutual friends will likely makesuggestions to those users with many connections within the socialnetworking system when compared to users with few connections within thesocial networking system. This leads to a sub-optimal result for thesocial networking system, since an additional friend for a user withmany friends is less valuable than an additional friend for a user withrelatively few friends. Other suggestion systems similarly focus onsimply adding connections among users without regard to the result ofthe suggested connections. Accordingly, existing mechanisms that suggestnew connections to a user based merely on indicators of a likelyconnection fail to address the value of the resulting connections to thesocial networking system.

SUMMARY

To enhance the experience of the users of a social networking systemwhile increasing value to the social networking system, embodiments ofthe invention suggest that a user takes an action within the socialnetworking system, where the suggestion is based on the likelihood thatthe user will perform the suggested action as well as the value to thesocial networking system if the user does so. In one embodiment, thesuggested action may include any interaction within the socialnetworking system, such as joining a group, becoming a fan of a non-userentity, liking an object or an advertisement, confirming attendance atan event, sending a message to another user, tagging a user in an image,or any other type of interaction a user may perform within the socialnetworking system. In another embodiment, the suggested action is forthe user to form a new connection or request a new connection withanother user of the social networking system. In other embodiments, thesuggested action may include a user forming a connection with orrequesting a new connection with an object, event, advertisement, entityor concept within the social networking system. The suggestions may beprovided to a user in a certain portion of an interface, such as aportion of a web page that contains other content. Alternatively, thesuggestions may be provided responsive to an action by the user, such asthe submission of a search query, accepting or sending a connectionrequest, liking or commenting on an item, posting or answering aquestion on a social network.

In an embodiment of the invention wherein the social networking systemsuggests a user to form a connection or request a connection with otherusers of the social networking system, a list of candidate users notassociated with the user is generated. In one embodiment, a first listof users is generated comprising users who are friends with the user,have requested a friendship with the user, have been suggested asfriends by other users of the social networking system and usersimported from the user's contact book outside the social networkingsystem is generated. A candidate list of users is generated byretrieving the friends of users in the first list who are not friendswith the user.

For each candidate user, the social networking system computes theprobability score that the user will form a friendship with eachcandidate user within the social networking system. In one embodiment,the social networking system uses machine learning algorithm andcomputes a probability of connection based on historical data.

The social networking system also computes a social network value scorefor each candidate, where the value score is indicative of the value tothe social networking system if the candidate user forms a connectionwith the user. Although the social networking system may define anynumber of functions or algorithms for computing the value score, in oneembodiment the score is based on a function that weighs new connectionsfor less active users more highly than new connections for active usersof the social networking system, thereby targeting less active users forincreased engagement with the social networking system.

The probability score and the value scores are used to determine anexpected value to the social networking system for suggesting eachcandidate user as a suggested connection to the user. The socialnetworking system then determines one or more suggestions to provide tothe particular user based on these expected values.

In one embodiment, once a candidate user is suggested to the user, thecandidate user's expected value score is discounted, thereby loweringthe expected value score and user ranking. New suggestions are providedto the user based on the discounted expected value score to suggest newcandidate users to the user. In one embodiment, the process isiteratively repeated.

Although discussed above in terms of suggesting that a user form a newconnection with another user, other embodiments of the invention mayprovide any object, entity, advertisement or concept associated with thesocial networking system can be suggested as new connections to theuser. Additionally, in other embodiments of the invention, the socialnetworking system can provide different suggestions to the user. Asexplained above, any type of activity within the social networkingsystem can be suggested to a particular user, where the suggestedactivity is selected based on the likelihood that the user will followthe suggestion as well as the value to the social networking system ifthe user does follow the suggestion. In this way, the suggestion systemcan maximize or increase the expected value to the social networkingsystem, rather than merely maximize the likelihood of a successfulsuggestion. Additionally, while embodiments of the invention discuss thesuggestions in terms of a passive suggestion that a user receives in auser interface, embodiments of the invention include providing the userwith a response to a user-initiated action, such as a search query.

The disclosed embodiments of the invention provide users to easily makeconnections with other users they may know. Such connections allow usersto more effectively interact with other users. The interactions mayprovide a user with a more meaningful user experience within the socialnetworking system. Consequently, the social networking system may have amore engaged user base and may lead to higher use of the socialnetworking system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system for ranking users based on a predictionof whether two users will connect within the social networking systemand the value of the connection to the social networking system, inaccordance with an embodiment of the invention.

FIG. 2 is a high level block diagram of a system, in accordance with anembodiment of the invention.

FIG. 3 is a flow chart of a process for computing an expected valuescore for users of a social networking system, in accordance with anembodiment of the invention.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION Configuration Overview

A social networking system offers its users the ability to communicateand interact with other users of the social networking system. As usedherein, a “user” can be an individual or an entity (such as a businessor third party application). The term “connection” refers individualsand entities with which a user of the social networking service may forma connection, association, or other relationship. In use, users join thesocial networking system and then connect with other users, individuals,and entities with whom they desire to be connected. Additionally, socialnetworking systems provide various communications channels for users tointeract with each other within the system. Thus, users of a socialnetworking system may interact with each other by “posting” contentitems of various types of media through the communication channels. Asusers increase their interactions with each other within the socialnetworking system, they engage with the social networking system on amore frequent basis. One method to increase user engagement with thesocial networking system is to facilitate an increase in userconnections within a social networking system such that additionalconnections are valuable to the social networking system.

FIG. 1 illustrates a system for ranking users of a social network basedon the probability of whether two users will form a connection with eachother within the social networking system and the value of theconnection to the social networking system. The system comprises auser's social networking data 100, a candidate set generator 130, aconversion prediction engine 150, a value computation engine 160, anexpected value scoring engine 280 and a database of expected valuerankings 170 assigned to each candidate user. On a high level, acandidate set of user not associated with the user is generated by thecandidate set generator 130 by accessing data within the socialnetworking system data 100. The conversion prediction engine 150receives the list of candidates and determines the value to the socialnetworking system for each connection between the user and eachcandidate user. Similarly, the value computation engine 160 receives alist of candidates and determines the value to the social networkingsystem for each candidate user if the candidate user forms a connectionwith the user. The expected value computation engine receivesprobabilities from the conversion prediction engine 150 and values fromthe value computation engine 160 to compute an expected value score foreach candidate user. Each candidate user is subsequently ranked in adatabase 170 based on each candidate's expected value score. In oneembodiment, the highest ranked candidates can be displayed to the useras suggested connection the user may know, either within or outside thesocial network. In other embodiments, other items can be ranked usingthe system described above.

In one embodiment, a user's social networking system data 100 comprisesthe user's friend network 102, friend request data 104, friendsuggestion data 106, and the user's contact file 108. A friend network102 comprises the names and associated information of all the users whohave formed a connection with the user. For example, if a user hasaccepted the first user's friend request, the two users have formed aconnection or a friendship within the social networking system. Eachuser's name would appear on the other's friend network. The friendrequest data 104 comprises the names and associated information of userwho have requested that the user add the users to the first user'sfriend network 102. The friend suggestion data 106 comprises name andassociated information of users who are suggested as friends either byother friends of the user, mutual friends of the users or by the socialnetworking system. The user's contact files include the name andassociated information of all the user within the social networkingsystem that the user has communicated with, either through email,instant messaging, text messaging, wall posts within the social network.These are just a few examples of the interactions which a user canengage in within the social networking system, and many others arepossible, and described in greater detail below. Thus, according to oneembodiment, the social networking system data 100 comprises all theusers of the social networking system the user has communicated with orbeen in contact with on some level within the social networking system.

The candidate set generator 130 generates a list of candidate users byaccessing the data stored within the social networking system data 100.In one embodiment, the candidate set generator 130 generates a list ofother users within the social networking system who have not yet formeda connection with the user but who may be associated in some way to theuser. Various embodiments for determining candidate users are describedin greater detail in reference to FIG. 3

The conversion prediction engine 150 receives the list of candidateusers generated by the candidate set generator 130 and determines theprobability associated with each candidate user forming a connectionwith the user within the social networking system. In one embodiment,the conversion prediction engine 150 uses historic demographic andbehavioral data associated with all user connections within the socialnetworking system to determine a probability of a connection. Otherembodiments for determining a probability score are described in greaterdetail below in reference to FIG. 3.

The value computation engine 160 receives a list of candidates from thecandidate set generator 130 to determine the value of connection betweenthe user and the candidate users to the social networking system. Thevalue computation engine 160 uses historic demographic, behavioral andusage data to determine a social system value score for each candidateuser.

The expected value scoring engine 280 uses the probability scorereceived from the conversion prediction engine 150 and the social systemvalue score from the value computation engine to determine an expectedvalue score for each candidate user. The expected value scoring engineoutputs a list of candidate users ranked by their expected value score170.

The expected value rankings 170 can be used by the social networkingsystem to provide numerous suggestions to the user. Embodiments of thesuggestions are described in greater detail below in reference to FIG.3. As described above, the candidate set generator 130, the conversionprediction engine 150, the value computation engine 160 and the expectedvalue scoring engine 280 are described in greater detail below inreference to FIGS. 2 and 3.

System Architecture

FIG. 2 is a high level block diagram illustrating a system for scoringusers based on a probability of connection with a first user and thevalue of connection to the social networking system, in accordance withan embodiment of the invention. The social networking system 200includes an application programming interface (API) request server 220,a web server 230, a message server 240, a user profile store 250, anaction logger 260, an action log 140, a connection store 270, acandidate set generator 130, a conversion prediction engine 150, a valuecomputation engine 160, an expected value scoring engine 280 and adiscount engine 290. In other embodiments, the social networking system200 may include additional, fewer, or different modules for variousapplications. Conventional components such as network interfaces,security mechanisms, load balancers, failover servers, management andnetwork operations consoles, and the like are not shown so as to notobscure the details of the system.

In some embodiments, the system 200 is not a social networking systembut communicates with a social networking system to obtain the necessarysocial network information. The system 200 may communicate with thesocial networking system, for example, using APIs provided by the socialnetworking system. In these embodiments, some modules shown in FIG. 2may run in the system 200, whereas other modules may run in the remotesocial networking system. For example, the modules including the aconversion prediction engine 150 and value computation engine 160,expected value scoring engine 280 and others may run in the system 200but modules API request server 220, user profile store 250, connectionstore 270, and action log 140 may exist in a separate social networkingsystem.

The social networking system 200 allows users to communicate orotherwise interact with each other and access content, as describedherein. The social networking system 200 stores user profiles in theuser profile store 250. The user profile information stored in userprofile store 250 describes the users of the social networking system200, including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, gender,sexual preferences, hobbies or preferences, location, and the like. Theuser profile may also store information provided by the user, forexample, images. In certain embodiments, images of users may be taggedwith the identification information of the appropriate users whoseimages are displayed.

The social networking system 200 further stores data describing one ormore connections between different users in the connection store 270.The connections are defined by users, allowing users to specify theirrelationships with other users. For example, the connections allow usersto generate relationships with other users that parallel the users'real-life relationships, such as friends, co-workers, partners, and soforth. In one embodiment, the connection specifies a connection typebased on the type of relationship, for example, family, or friend, orcolleague. Users may select from predefined types of connections, ordefine their own connection types as needed.

The web server 230 links the social networking system 200 via a networkto one or more client devices; the web server 230 serves web pages, aswell as other web-related content, such as Java, Flash, XML, and soforth. The web server 230 may communicate with the message server 240that provides the functionality of receiving and routing messagesbetween the social networking system 200 and client devices. Themessages processed by the message server 240 can be instant messages,queued messages (e.g., email), text and SMS (short message service)messages, or any other suitable messaging technique. In someembodiments, a message sent by a user to another can be viewed by otherusers of the social networking system 200, for example, by theconnections of the user receiving the message. An example of a type ofmessage that can be viewed by other users of the social networkingsystem besides the recipient of the message is a wall post. In someembodiments, a user can send a message can send a private message toanother user that can only be retrieved by the other user.

The action logger 260 is capable of receiving communications from theweb server 230 about user actions on and/or off the social networkingsystem 200. The action logger 260 populates the action log 140 withinformation about user actions to track them. Any action that aparticular user takes with respect to another user is associated witheach user's profile, through information maintained in a database orother data repository, such as the action log 140. Such actions mayinclude, for example, adding a connection to the other user, sending amessage to the other user, reading a message from the other user,viewing content associated with the other user, attending an eventposted by another user, being tagged in photos with another user, likingan entity, among others. In addition, a number of actions describedbelow in connection with other objects are directed at particular users,so these actions are associated with those users as well. When a usertakes an action on the social networking system 200, the action isrecorded in an action log 140. In one embodiment, the social networkingsystem 200 maintains the action log 140 as a database of entries. Whenan action is taken on the social networking system 200, the socialnetworking system 200 adds an entry for that action to the log 140.

The API request server 220 allows external systems to access informationfrom the social networking system 200 by calling APIs. The informationprovided by the social network may include user profile information orthe connection information of users as determined by their individualprivacy settings. For example, a system interested in predicting theprobability of users forming a connection within a social networkingsystem may send an API request to the social networking system 200 via anetwork. The API request is received at the social networking system 200by the API request server 220. The API request server 220 processes therequest by determining the appropriate response, which is thencommunicated back to the requesting system via a network.

Continuing with FIG. 2 the candidate set generator 130 populates a setof users who may interact with a first user outside the socialnetworking system but not connected to the first user within the socialnetworking system. In one embodiment, the candidate set generator 130populates a first list comprising of the user's friends, users who senta friend request to the user, users suggested as friends by other usersof the social networking system and users imported from the user's emailcontacts, if available. Additionally, the candidate set generator 130populates a set of users who are connected to the users in the firstlist. The candidate set generator 130 removes the names of all usersfrom the list who are already connected to the user. Thus, the candidatelist generator creates a candidate list of friends of the user's friendswho not connected to the user within the social networking system. Inother embodiments, the candidate set generator 130 can generate a listof candidate users comprising friends of the user's friends who alsoshare certain similar characteristics with the user. A list of similarcharacteristics includes, but is not limited to: sharing a socialnetwork, similar college or high school graduation year, checking intothe social network from the same location at about the same time, etc.In another embodiment, the candidate list generator 130 generates acandidate list of user by selecting a portion of the friends of theuser's friends not connected to the user by using a heuristics to narrowthe field or linear-time rank algorithm to find a cutoff point toexclude the friends of the user's friends.

The conversion prediction engine 150 predicts whether a user will act onthe friend suggestion. In one embodiment, the conversion predictionengine 150 is a machine learning model trained using a set of historicaldata. Historical data includes, but is not limited to demographic data,behavioral data and communications data of a user within the socialnetworking system. For example, the conversion prediction engine 150 canuse data associated with the user and each candidate user, such as thenumber of friends in common, the length of time two users have beenfriends, ratio of mutual friends to total friends, age, gender, country,total friends, time spent on the social networking system, the length oftime the users have been associated with the social networking systemetc to predict the likelihood of a connection between the user. In oneembodiment, a training set is generated using historical data of usersto whom previous suggestions were made, data about the users who werethe subject of the suggestion and whether the user acted on thesuggestion. In one embodiment the conversion prediction engine 150 istrained using the training set data. Subsequently, the user andcandidate user data is input to the conversion prediction engine 150.The conversion prediction engine 150 outputs the probability that theuser will act on the friend or connection suggestion for each candidateuser. For example, the conversion prediction engine 150 may find adiscount factor associated with users who have been friends for a longperiod of time within the social networking system based on data thatnew connections of a recently added friend may be more likely to be amutual friend of the user.

The value computation engine 160 determines a value of a connection tothe social networking system. In one embodiment the value computationengine 160 is an assignment system for assigning a pre-determinedarbitrary value to a particular type of connection within the socialnetworking system. For example, having only one association within thesocial networking module may have a high score associated with it. Thevalue computation engine 160 assigns the high score to the candidateuser with only one association within the social networking system.

The expected value scoring engine 280 is a computation algorithm used tocombine the probability score with the value computation score. In oneembodiment, the value scoring engine 280 multiples the probability scoredetermined by the conversion prediction engine 150 with the value scoredetermined by the value computation engine 160. In other embodiments,the expected value scoring engine 280 takes the sum of multiple valuesof the probability score and the value score and their probability ofoccurring to generate an expected value score.

The social networking system suggests candidate users to the user basedon each candidate user's expected value score. For example, the socialnetworking system can suggest candidate users with the highest expectedvalue score. In such an embodiment, the discount engine 290 discountsthe expected value score for each candidate user suggested to the user.In one embodiment, the discount engine 290 provides a discount valueassociated with each impression of a suggestion shown to the user. Inanother embodiment, the discount engine 290 re-ranks each user based onthe number of user impressions shown to the user. Thus the discountengine enables the social networking system to make new suggestions withnew candidate users to the user.

Although discussed above in terms of suggesting that a user form a newconnection with another user, other embodiments of the invention mayprovide different suggestions to the user. As explained above, anycandidate connection can be suggested as a new connection to aparticular user. A candidate connection within the social networkingsystem can include any entity, object, advertisement, concept or userwithin the social networking system. In one embodiment, the socialnetworking system suggests a connection to a user based on thelikelihood that the user will establish or request a connection with thecandidate connection and the value of the connection between the userand the candidate connection to the social networking system. In oneembodiment, the social networking system suggests an entity such asPINKBERRY or a concept such as ‘running’ to a user based on theprobability of whether the users will form a connection with PINKBERRYor running, within the social networking system and the value of theconnection between PINKBERRY or running and the user. In one embodiment,the probability of a connection between the user and the object isdetermined by the conversion prediction engine 150 based on historicdemographic, behavioral and usage data associated with the user withinthe social networking system. Similarly, a value of the connection tothe social networking system is determined by the value computationengine 160 based on historic demographic and behavioral data associatedwith the user and the specifications provided by or the characteristicassociated with the object. For example, if an advertisement objectincludes targeting criteria, the value of a connection between theadvertisement and the user is greater if the user matches the providedadvertising criteria. Similar to the embodiments presented above for aconnection with the user, each candidate object receives an expectedvalue score based on a combination of the probability score and thesocial network value score calculated by the expected value scoringengine 280. Each object is ranked in a database 170 based on theexpected value score associated with the object. In one embodiment, thehighest ranked object is displayed to the user as a suggested connectionthe user may be associated with, either within or outside the socialnetworking system.

In other embodiments, the social networking system suggests actions auser can perform within the social networking system. Suggested actionswithin the social networking system can include joining a group,becoming a fan of a non-user entity, confirming attendance at an event,sending a message to another user, tagging a user in an image, or anyother type of interaction with an object that the user may performwithin the social networking system. In one embodiment, the candidateset generator 130 generates candidate suggestions responsive to areceived action suggestion. In one embodiment, action suggestions arereceived by the candidate set generator 130. Responsive to the actionsuggestions, the candidate set generator 130 generates candidatesuggestions. For example, if the candidate suggestion is performing anaction within the social networking system, such as messaging, poking,tagging, the candidate set generator 130 generates a candidate list ofusers with whom the user can perform the suggested actions within thesocial networking system. In such an embodiment, the candidate setgenerator 130 retrieves messaging, poking or tagging data for the userand suggests other users who have not been messaged, poked or tagged bythe user. If on the other hand candidate suggestion is playing videogames, for example, the candidate set generator 130 generates a list ofcandidate games the user can play within the social networking system.In such an embodiment, the candidate set generator 130 retrieves userdata regarding video game usage within the social networking system.Based on the type of game played, duration and frequency of play,interactivity, challenge and other factors, the candidate set generator130 generates a set of games never played by to user but with highaffinity to the factors considered, such as type of game, duration andfrequency of play etc.

Similar to the embodiments described above with regards to suggestingcandidate users, the conversion prediction engine 150 uses machinelearning to determine, for example the probability that the user willperform other actions within the social networking system such as send amessage to a candidate user or play a particular video game based onhistorical data. Additionally, the expected value scoring engine 280assigns a social network value score to a particular action based on thevalue of the action to the social networking system. For example, if thesocial networking system highly values playing a particular video gamewithin the social networking system, the expected value scoring engine280 provides a high score to the particular video game. The expectedvalue score is provided by the social networking system and can beupdated to reflect changes in value of performing a particular actionwithin the social networking system. The value computation engine 160combines the prediction score and the social network value score todetermine an expected value score similarly to the embodiments describedabove. Once an expected value score is determined for each candidateaction, each candidate action can be ranked, wherein the highest rank isassociated with the highest expected value. The social networking systemsuggests actions to the user based on the ranking. Once an action issuggested to the user the expected value discount engine 290 discountsthe expected value score for each action suggested to the user similarlyto the embodiments described above. In such an embodiment, the socialnetworking system can iteratively discount expected value and suggestaction to the user.

Method of Ranking Candidate Users according to an Expected Value Score

FIG. 3 illustrates an embodiment of a process 300 for suggesting friendsto a user within the social networking system. In one embodiment, a useris one for whom the social networking system needs suggestion. Forexample, if a user navigates to a page within the social networkingsystem providing friend suggestions, the process 300 is executed inorder to provide friends suggestions for the user.

The process 300 generates 305 a list of candidate users not connectedwith the user. In one embodiment, the process 300 generates a first listof user associated with the user. For example, process 300 accessessocial networking system data 100 to retrieve friend network data 102,friend request data 104, friend suggestions data 106 and user's contactfile data 108. In such an embodiment, the process generates the firstlist of associations comprising the user's friends from the friendnetwork data 102, users who have requested a friendship with the userfrom the friend request data 104, users who have been suggested asfriends to the users by other users of the social networking system fromfriend suggestions data 106 and users imported from the user's contactbook, such as OUTLOOK contacts from the user's contact files 108. Oncethe first list of associations is generated, the process 300 uses thefirst list to generate 305 a list of users not associated with the user.In one embodiment, the process 300 generates 305 a list of candidateusers by retrieving all users associated with the users on the firstlist. Additionally, the process generates 305 a list of candidate usersby removing from the candidate list, all users who are connected withthe first users, such as the users on the first list. In otherembodiments, the process 300 generates 305 a list of candidate usersbased on data within the social networking system. For example, theprocess 300 can search demographic data similar to the user. If anotheruser matches the user's demographic data, including but not limited to,name of the high school or college, graduation year, workplace name etc.Additionally, the process can also include users who check into thesocial networking system from about the same location as the user atabout the same time. Finally, the process 300 removes all users who arealready associated with the user within the social networking system.Thus, the process generates 305 a list of candidate users not associatedto the user within the social networking system.

The process 300 computes 310 the probability of the user becomingfriends with each candidate user. In one embodiment, the processexecutes a prediction algorithm. A prediction algorithm can be a machinelearning model trained using historical data. For example, the process300 receives historical data such as demographic data, behavioral dataor communications data for a user. Historical data includes, but is notlimited to data about a user's age, number of friends, work status,school status, activity level including the amount of interaction withother users within the social networking system, the number of loginsper day, the number of refreshes per day, the amount of photos or videosuploaded, etc. The process receives historical data for users andprovides the data as signals to the conversion prediction engine 150 totrain a probabilistic model.

In one embodiment, the process identifies two or more users withhistorical data that includes information about the user to whomprevious suggestions were made, information about the users who were thesubject of the suggestion and whether the user acted on the suggestion.

In one embodiment, information on users includes: the number of friendsin common, whether the users are part of the same primary network, thenumber of networks in common, political affiliations, the difference inage, the gender of the users, home geographic ID, home country, currentcountry, work history including, work company overlap, work company timeoverlap; school history including school overlap, school type overlap,etc.

Additionally, the process 300 provides historical data for the user andeach candidate user to the conversion prediction engine as signals. Insuch an embodiment, the process 300 computes 310 the probability of theuser and each candidate user becoming friends by executing theconversion prediction engine 150 algorithm. Thus, each candidate user israted with a conversion probability score.

In another embodiment, the process 300 computes the probability ofconversion 310 using a predetermined probability score which is based onsome understanding of user relationships within the social networkingsystem. For example, the candidate users can be predicted to be friendswith the user if the candidate users have a certain number of friends incommon with the user.

A social network value score is assigned 330 by the process 300. Asocial network value score is a measure of value of a connection to thesocial networking system. In one embodiment the social network valuescore is based on engagement level within the social networking system.For example, the process assigns 315 a higher social network value scoreto a candidate user if the user's frequency of social networking systemusage is low. In other embodiments, the process assigns 315 a highersocial network value score to candidate users with few friends withinthe social networking system, or part of a particular demographic suchas age range or gender, etc. The social network value score allow thesocial networking system to preferentially engage certain users of thesocial networking system. By suggesting friends with low activity levelwithin the social networking system, the system can enable those usersto likely have more friends as a result being suggested more than userswith lower social network value score and thereby likely increasing thecandidate user's engagement level with the social networking system.

The process computes 320 an expected value score once the probability ofa conversion is computed 310 and the network value score is assigned315. An expected value score is a combination of the probability scoreand the network value score. In one embodiment the two score arecombined by multiplying the scores. In another embodiment, the combinedscore or the expected value score is computed 320 by weighing one of theconversion or value scores more heavily based on the social networkingsystem's targeting criteria or preferences. The expected value score isused to rank each candidate user wherein a best ranking is assigned tothe user with the best conversion prediction score and the best valuescore or the best combined score. In one embodiment, the process 300ranks candidate users according to their computed expected score suchthat candidate users with the highest expected value score are rankedthe highest.

The process suggests 330 candidate users with the highest expected valuescore to the user. In one embodiment, candidate users are suggested 325by displaying the candidate user profile to the user. In anotherembodiment, a candidate user is suggested 325 by sending a message tothe user indicating a friend suggestion. In other embodiments, othermethods of notifying the user of a friend suggestion can be used. In oneembodiment, candidate users below a certain threshold score are notsuggested 325 to the user. In another embodiment, candidate users belowa certain ranking are not suggested 325 to the user.

Once a candidate user is suggested to the user one or more times, theprocess discounts the candidate user's ranking. In one embodiment, eachtime a candidate user is shown to the user, the process discounts 330the candidate user's expected value score, thereby lowering thecandidate's ranking. In one embodiment, the process discounts 330candidate users based on the frequency with which a candidate users issuggested to the user. In another embodiment, the process applies adiscount based on the how the candidate user was suggested to the user.For example, an email suggestion may carry a higher discount score thandisplaying a candidate user on a portion on a web page within the socialnetworking system. Since the process discounts 330 expected value scoresof candidate users already suggested to the user, it tends to bumpsuggested candidate users down the list of ranked candidates, allowingthe social networking system to make new suggestions with new candidateusers. The process of discounting 330 expected value score and makingnew suggestions 325 is iteratively repeated. It should be noted that insome embodiments, candidate users below a certain threshold expectedvalue score are not suggested 325 by the process 300.

Although discussed above in terms of suggesting that a user form a newconnection with another user, other embodiments of the invention mayprovide different suggestions to the user. As explained above, any typeof activity within the social networking system can be suggested to aparticular user, where the suggested activity is selected based on thelikelihood that the user will follow the suggestion as well as the valueto the social networking system if the user does follow the suggestion.

In such an embodiment, the process generates a list of candidate actionsa user can perform within the social networking system responsive to oneor more actions specified by the social networking system. For example,the social networking system can provide a candidate action, such assending a message to another user, or tagging another user in a picture,or poking another user. In such an embodiment, the process generates acandidate list of users whom the user can send a message to, or poke ortag in an picture uploaded to the social networking system. On the otherhand, the social networking system can provide, for example, playingvideo game as a candidate action. In such an embodiment, the process 300generates a list of candidate video games that the user has not played.For example, the process can generate a list of factors associated withvideo games a user currently plays within the social networking system,such as, the type of game, the frequency and duration of play, the levelof interactivity required etc. Based on the factors, the processgenerates 300 a candidate set of games the user has not played tosuggest to the user.

Similar to the embodiments described above in reference to suggestingcandidate users, the process computes 300 a probability of a user a usersending a message to each candidate user or playing each candidate videogame, for example. Additionally, the process assigns 300 a socialnetwork value score to each candidate user or video game based on thevalue score provided by the social networking system. The social networkvalue score dependant on whether the social networking system wants toencourage a particular action, such as send a message to a particularcandidate user or playing a particular video game. Wherein, a highervalue score is associated with an action the social networking systemwants to encourage. Similar to the embodiments described above, theprocess 300 computes an expected value score from the probability scoreand the value score. Furthermore, the process suggests actions to theuser based on the expected value score, wherein the candidate actionsreceiving the highest expected value score are displayed first to theuser. Additionally, the process 300 also discounts the expected valuescore for each action suggested to the user. The process 300 forsuggesting actions within the social networking system is iterativelyrepeated such that once an action is suggested to the user, the action'sexpected value is discounted, generating a new expected value for theaction. Thus, the social networking system can make new suggestions tothe user with new suggested actions.

SUMMARY

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the invention may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method for suggesting new connections to a userof a social networking system, the method comprising: generating a listof candidate connections not connected to the user in the socialnetworking system; predicting the likelihood of a connection between theuser and each candidate connection, the likelihood calculated responsiveto historical user data within the social networking system; determininga value to the social networking system of a new connection between theuser and each candidate connection, the value based on data associatedwith the user and the connection in the social networking system;computing an expected value for each candidate connection, the expectedvalue based on the predicted likelihood of a connection between the userand the candidate connection and the determined value to the socialnetworking system of a new connection between the user and the candidateconnection; and suggesting one or more candidate connections to theuser, the candidate connections suggested based on the computed expectedvalue associated with each candidate connection.
 2. The method of claim1, wherein candidate connections include objects, entities, concepts orother users not connected with the user within the social networkingsystem.
 3. The method of claim 1, wherein predicting the likelihoodcomprises using a machine learning algorithm, the machine learningalgorithm trained using historical data collected about userinteractions in the social networking system.
 4. The method of claim 1,wherein the predicting the likelihood is based on one or more of thenumber of mutual connections between the user and the candidateconnection, including the number of mutual connections, the ratio ofmutual connections to total connections, time discounted mutual friends,common networks, gender, high school, college, graduation year,interests, favorite books, movies, television shows, music, beingidentified in the same pictures, the number of times the user wasdisplayed a picture of each candidate connection, the number of timesthe user clicked on a link of each candidate connection.
 5. The methodof claim 1, wherein determining the value to the social networkingsystem of a new connection between the user and each candidateconnection is based at least in part on a number of existing connectionsof the candidate connection, wherein the value of a new connection ishigher when the number of existing connections is lower.
 6. The methodof claim 1, wherein determining the value to the social networkingsystem of a new connection between the user and each candidateconnection is based at least in part on a frequency of social networkingsystem usage of the candidate connection, wherein the value of a newconnection is higher when the frequency of social networking systemusage is lower.
 7. The method of claim 1, wherein determining the valueto the social networking system of a new connection between the user andeach candidate user is based at least in part on a time since joiningthe social networking system of the candidate user, wherein the value ofa new connection is higher when the time since joining the socialnetworking system is lower.
 8. The method of claim 1, whereindetermining the value to the social networking system of a newconnection between the user and each candidate user is based at least inpart on demographic information about the candidate user.
 9. The methodof claim 1, wherein the one or more suggestions are provided to the userin a page of information, without the user requesting the suggestions.10. The method of claim 1, wherein the one or more suggestions areprovided to the user responsive to a query from the user forsuggestions.
 11. The method of claim 1, further comprising discountingthe expected values associated with each of the candidate userssuggested to the user.
 12. The method of claim 1, further comprising:discounting the expected values associated with each of the candidateconnection suggested to the user; and suggesting one or more candidateconnection to the user, the suggestions based on the discounted expectedvalues associated with each of the candidate users.
 13. The method ofclaim 11, wherein discounting the expected value associated with eachcandidate connection is a function of the number of times the candidateconnection has been previously suggested to the user.
 14. The method ofclaim 1, further comprising: selecting one or more additional candidateconnections to suggest to the user to add as a new connection in thesocial networking system based on discounted expected values; andproviding the additional suggestions to the user.
 15. A method forsuggesting an action to a user of a social networking system, the methodcomprising: generating a list of actions comprising candidate actionsavailable to a user of the social networking system; predicting thelikelihood that a user will perform each candidate action, thelikelihood calculated responsive to historical data associated with theuser within the social networking system; determining a value to thesocial networking system of the user performing each candidate action;computing an expected value for each candidate action, the expectedvalue based on the predicted likelihood of the user performing thecandidate action and the determined value to the social networkingsystem of the user performing the candidate action; and suggesting oneor more candidate actions to the user, the candidate action suggestedbased on the computed expected value associated with each candidateaction.
 16. The method of claim 15, wherein the list of actionscomprises candidate actions the user can perform within the socialnetworking system and a list of connections, the connections capable ofinteracting with the user through the candidate actions.
 16. The methodof claim 15, wherein the candidate action comprises an action selectedfrom a group comprising sending a message to a connection, sending aninstant message to a connection, posting text on a connection's profilepage, sending a virtual gift to a connection, commenting on aconnection's activity or broadcast post, commenting on a connection'spictures, playing a particular game among a group of games, updating theuser's profile page, replying to an invitation, becoming a fan of apage, and inviting another user to a group.
 18. The method of claim 15,wherein the candidate action comprises of playing a video game withinthe social networking system and the candidate list of actions comprisea list of video games available for the user to play within the socialnetworking system.
 19. The method of claim 15, wherein predicting thelikelihood comprises using a machine learning algorithm, the machinelearning algorithm analyzing historical, behavioral and interaction dataof users of the social networking system to predict the probability of auser performing a particular action within the social networking system.20. The method of claim 15, wherein determining the value to the socialnetworking system of a new action suggested to the user is based atleast in part on a number of prior actions performed by the user,wherein the value of a suggested action is higher when the number ofexisting actions is lower.
 21. The method of claim 15, whereindetermining the value to the social networking system of a new actionsuggested to the user is based at least in part on a number of existingconnections of the user or the connection, wherein the value of asuggested action is higher when the number of existing connections islower.
 22. The method of claim 15, wherein determining the value to thesocial networking system of a new action suggested to the user is basedat least in part on a frequency of social networking system usage of theuser, wherein the value of a suggested action is higher when thefrequency of social networking system usage is lower.
 23. The method ofclaim 15, wherein determining the value to the social networking systemof a new action suggested to the user is based at least in part on atime since joining the social networking system of the user, wherein thevalue of a suggested action is higher when the time since joining thesocial networking system is lower.
 24. The method of claim 15, whereindetermining the value to the social networking system of a new actionsuggested to the user is based at least in part on demographicinformation about the user.
 25. The method of claim 15, wherein the oneor more suggestions are provided to the user in a page of information,without the user requesting the suggestions.
 26. The method of claim 15,wherein the one or more suggestions are provided to the user responsiveto a query from the user for suggestions.
 27. The method of claim 15,further comprising discounting the expected values associated with eachof the candidate actions suggested to the user.
 28. The method of claim15, further comprising discounting the expected values associated witheach of the candidate actions suggested to the user; and suggesting oneor more candidate actions to the user, the suggestions based on thediscounted expected values associated with each of the candidateactions.
 29. The method of claim 15, wherein discounting the expectedvalue associated with each candidate action is a function of the numberof times the candidate action has been previously suggested to the user.